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Course Outline

  1. Distributed Computing in Large-Scale Data Environments
    1. Data extraction techniques (individual model training and distributed inference: conventional machine learning algorithms and MapReduce-based prediction) /li>
    2. Apache Spark MLlib integration
  2. Recommendation Systems and Targeted Advertising
    1. Fundamentals of natural language processing /li>
    2. Text clustering, categorization (labeling), and synonym identification
    3. Reconstruction of user profiles and tagging frameworks /li>
    4. Strategies for optimizing recommendation algorithms /li>
    5. Intra-class and inter-class lift analysis; methods for achieving precision /li>
    6. Establishing closed-loop feedback mechanisms for recommendation algorithms
  3. Logistic Regression and Ranking SVM techniques /li>
  4. Feature identification: (automated feature recognition utilizing deep learning and graph structures) /li>
  5. Natural Language Processing
    1. Chinese word segmentation techniques /li>
    2. Topic modeling (text clustering applications) /li>
    3. Text classification methodologies /li>
    4. Keyword extraction processes /li>
    5. Semantic analysis: semantic parsers and Word2Vec vector representations /li>
    6. Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) architectures

Requirements

No specific prerequisites are required for participation in this course.

 21 Hours

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